SWEAT: Scoring Polarization of Topics across Different Corpora

09/15/2021
by   Federico Bianchi, et al.
0

Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.

READ FULL TEXT
research
01/19/2018

Size vs. Structure in Training Corpora for Word Embedding Models: Araneum Russicum Maximum and Russian National Corpus

In this paper, we present a distributional word embedding model trained ...
research
03/07/2019

Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain

Word embeddings are already well studied in the general domain, usually ...
research
07/05/2020

Improving Chinese Segmentation-free Word Embedding With Unsupervised Association Measure

Recent work on segmentation-free word embedding(sembei) developed a new ...
research
02/28/2023

Goal Driven Discovery of Distributional Differences via Language Descriptions

Mining large corpora can generate useful discoveries but is time-consumi...
research
11/25/2017

Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity

We evaluate 8 different word embedding models on their usefulness for pr...
research
05/19/2023

Evaluating task understanding through multilingual consistency: A ChatGPT case study

At the staggering pace with which the capabilities of large language mod...
research
07/07/2022

Word Embedding for Social Sciences: An Interdisciplinary Survey

To extract essential information from complex data, computer scientists ...

Please sign up or login with your details

Forgot password? Click here to reset